Dynamic Resource Manager for Automating Deployments in the Computing Continuum

TL;DR

This paper introduces a unified resource manager framework that automates deployment and maintains performance across diverse and dynamic edge-cloud environments, addressing challenges in the computing continuum.

Contribution

It presents a novel seamless resource manager that dynamically allocates and reallocates resources across heterogeneous platforms to meet SLOs in real-time.

Findings

Effective automation of application deployment across multiple platforms.

Prompt detection and reallocation of resources upon SLO violations.

Minimal overhead in monitoring and reallocation processes.

Abstract

With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum includes diverse cloud, edge, and endpoint devices, posing challenges for software design due to varied hardware options. To tackle this, a unified resource manager is needed to automate and facilitate the use of the computing continuum with different types of resources for flexible software deployments while maintaining consistent performance. Therefore, we propose a seamless resource manager framework for automated infrastructure deployment that leverages resources from different providers across heterogeneous and dynamic Edge-Cloud resources, ensuring certain Service Level Objectives (SLOs). Our proposed resource manager continuously monitors SLOs and reallocates resources promptly in case of violations to prevent disruptions and ensure steady performance. The experimental results across serverless and serverful platforms demonstrate that our resource manager effectively automates application deployment across various layers and platforms while detecting SLO violations with minimal overhead.